ML-инженер вакансии

• Опыт работы в сфере AI/ML от 5 лет.

Предстоит: Участвовать в планировании и запуске A/B-тестов, уметь интерпретировать результаты; Работать в связке с Data Scientist, Data Analyst: собирать требования, формировать и декомпозировать задачи, синхронизировать команду по статусам;

— Участвовать в управлении ML-продуктами: анализировать технические и продуктовые метрики, предлагать гипотезы, искать точки роста. — Участвовать в планировании и запуске A/B-тестов, уметь интерпретировать результаты.

разрабатывать и дообучать модели TTS: проектирование, finetuning и адаптация генеративных подходов для высококачественного синтеза речи и zero shot клонирования голоса под задачи бизнеса; участвовать в дальнейшей разработке голосового пайплайна, объединяющего ASR, VAD, LLM, NLP и TTS для работы в режиме реального времени;

Роль предполагает разработку и внедрение моделей машинного обучения, анализ данных, решение бизнес-задач с помощью ML и взаимодействие с командой для создания и улучшения интеллектуальных решений.

Location: United States of America Why Join Exadel We’re an AI-first global tech company with 25+ years of engineering leadership, 2,000+ team members, and 500+ active projects powering Fortune 500 clients, including HBO, Microsoft, Google, and Starbucks. From AI platforms to digital transformation, we partner with enterprise leaders to build what’s next. What powers it all? Our people are ambitious, collaborative, and constantly evolving. About the Client A global technology leader specializing in search technology, digital advertising, cloud computing, and artificial intelligence. Delivering enterprise cloud infrastructure and software solutions, the organization builds scalable consumer and enterprise AI experiences. Today, the company leverages foundational AI models and advanced machine learning to power digital ecosystems for billions of global users. What You’ll Do Conduct formal, end-to-end written privacy reviews for products, features, research publications, data pipelines, and experimental prototypes Evaluate privacy risks and recommend practical safeguards aligned with internal standards and applicable global regulations Manage reviews from initial discovery and risk identification through remediation and final approval Provide strategic guidance and support the escalation of complex or high-risk privacy matters Maintain accurate, detailed, and audit-ready assessment documentation Assess the privacy implications of AI, machine learning, generative AI, and Large Language Model systems Evaluate data ingestion, prompt inputs, context windows, model outputs, training workflows, inference processes, and human-in-the-loop oversight Identify potential risks involving personal data, data leakage, memorization, secondary use, data retention, and inappropriate model outputs Recommend appropriate privacy controls for experimental and production AI systems Support the responsible development and launch of emerging AI capabilities Review technical design documents, Product Requirements Documents, Business Requirements Documents, system architecture diagrams, data-lineage documentation, and academic publications Identify privacy touchpoints, data dependencies, and potential gaps across product and data-processing lifecycles Translate privacy, legal, and compliance requirements into practical and actionable guidance for product and engineering teams Evaluate complex product scenarios in which requirements, risks, or ownership may not yet be clearly defined Partner with product managers, software engineers, data scientists, legal teams, and privacy stakeholders to incorporate privacy controls throughout the product lifecycle Evaluate controls relating to notice and consent, transparency, user choice, data portability, data minimization, access, and retention Review data-collection practices, consent flows, access controls, privacy policies, and user-facing privacy notices Help teams make informed product decisions that balance innovation, regulatory obligations, and user trust Identify gaps in existing privacy-review processes and recommend operational improvements Develop and refine privacy frameworks, standard operating procedures, guidelines, templates, and review criteria Support operational reporting and program-management activities across the privacy lifecycle Use custom tools and AI technologies to automate workflows, improve review efficiency, and optimize governance operations Help establish consistent and scalable privacy-review practices across research and innovation teams What You Bring Extensive hands-on experience conducting formal written privacy reviews, assessments, or impact analyses Product-focused privacy experience within technology, digital platforms, cloud services, AI, or similarly complex environments An active IAPP certification in at least one of the following: Artificial Intelligence Governance Professional, Certified Information Privacy Professional/United States, Certified Information Privacy Professional/Europe, Certified Information Privacy Manager Practical knowledge of AI and machine learning data-processing workflows, generative AI applications, and Large Language Models Understanding of privacy risks associated with model-training data, user inputs, inference processes, and model outputs Deep knowledge of privacy principles, including notice and consent, transparency, user choice, data portability, data minimization, and retention Knowledge of relevant privacy and AI regulatory frameworks, including GDPR, CCPA, and the EU AI Act Ability to interpret complex software-design documents, system architectures, data flows, and product requirements Experience managing multiple reviews and competing priorities under tight deadlines Strong judgment and the ability to make well-supported recommendations in ambiguous or rapidly evolving environments Exceptional written and verbal English communication skills Ability to translate legal and compliance requirements into clear, actionable technical guidance Strong stakeholder-management and influencing skills across product, engineering, legal, and governance teams Nice to have Familiarity with enterprise privacy-review, governance, and product-launch tools Experience supporting privacy governance for research, experimental products, or emerging technologies Experience working with cross-functional legal, ethics, privacy, product, and engineering governance bodies Experience automating privacy-review or governance workflows using AI or custom tooling A degree in Law, Computer Science, Data Governance, Information Security, or a related discipline Additional privacy, security, risk, or AI-governance certifications English level Advanced Work Type: Remote Location: United States Working Hours: Alignment with west coast US time zone Legal & Hiring Information Compensation Transparency: The expected compensation range for this role is $50–100/hr USD, with eligibility for commission, bonus, or other incentive compensation where applicable. Actual compensation will be determined based on experience, skills, qualifications, geographic location within the United States, and business needs. Location Eligibility: This role is open to candidates residing in the United States. Experience Requirements: We welcome applicants with relevant experience regardless of where it was obtained. US work experience is not required. Use of Artificial Intelligence in Hiring: We may use automated tools or artificial intelligence systems to support the screening, assessment, or selection of applicants as part of our hiring process. Benefits Summary: US-based employees are eligible to participate in Exadel’s benefits programs, which may include comprehensive health, dental, and vision coverage; life and disability insurance; retirement savings programs; paid time off; paid holidays; and other wellness or voluntary benefit programs, subject to plan terms and eligibility requirements. Equal Employment Opportunity: Exadel is proud to be an Equal Opportunity Employer committed to inclusion across minority, gender identity, sexual orientation, disability, age, and more. We do not discriminate on the basis of race, color, religion, sex, gender identity or expression, sexual orientation, national origin, age, disability, status as a protected veteran, or any other protected characteristic under applicable federal, state, and local laws. Accessibility and Accommodation: We are committed to providing accommodations throughout the recruitment process in accordance with the Americans with Disabilities Act (ADA) and other applicable federal, state, and local accessibility laws. Reasonable accommodations are available to enable individuals with disabilities to perform essential functions. If you require accommodation at any stage of the hiring process, please contact us. Data Privacy Notice: For applicants residing in California, please note that personal data collected during the application process is subject to the California Consumer Privacy Act (CCPA) and will be processed in accordance with our applicant privacy notice. Disclaimer: Please note: this job description is not exhaustive. Duties and responsibilities may evolve based on business needs. The offer is not binding until a signed contract is in place. Exadel Culture We lead with trust, respect, and purpose. We believe in open dialogue, creative freedom, and mentorship that helps you grow, lead, and make a real difference. Ours is a culture where ideas are challenged, voices are heard, and your impact matters.

Мы приглашаем в нашу команду опытного ML инженера для работы над проектом по созданию системы анализа данных.

• Разработка кросс-ассетных стратегий: Поиск альфа-сигналов, построение и валидация математических и ML-моде...

-Разрабатывать computer vision и machine learning решения для архитектурных чертежей, технических документов, CAD и BIM-данных. -Строить пайплайны, объединяющие растровые изображения, векторную геометрию, структурированные инженерные данные, OCR и ML-модели.

Развитие классификатора данных для полей БД (Tabular ML). Развитие существующей ML-модели (RandomForest), которая выявляет ПДн в полях баз данных на основе метаданных и значений.

• Обучать модели принятия решений для классификации, выбора и оценки; проверять калибровку вероятностей, качество и скорость инференса.

Mistral is looking for a Research Engineer GPU Performance to optimize generative AI model training and inference on GPUs.

• Опыт разработки прибыльных ML-моделей или торговых стратегий. • Знание Python, PyTorch/JAX.

Agile Robots SE is looking for a Senior Machine Learning Engineer Computer Vision for Robotics to develop and deploy real-time visual servoing models for robot manipulation.

Опыт оптимизации LLM-инференса с SGLang, vLLM или TensorRT-LLM. Работа с большими моделями на распределённой GPU-инфраструктуре.

Responsibilities Model development and selection: Lead the design of statistical and ML models (classification, regression, ranking, NLP or LLM-based) for problems like risk scoring, fraud detection and transaction categorization. Choose approaches on their merits and defend those choices with data, not habit.

Создавать ИИ-агентов с использованием LangChain и LangGraph. Глубокое понимание основ Python: типы данных, функции и классы, обработка исключений, итераторы и генераторы, context managers, модуль typing, внутренняя структура объектов и управление памятью.

Разработка и обучение моделей машинного обучения для задач NLP и компьютерного зрения; Анализ и обработка данных;